Traditionally, scan diagnosis is used to determine the most likely faulty locations and fault types for a given failing integrated circuit device (die). The diagnosis results guide physical failure analysis (PFA) to locate defects and identify the root cause. Defects are typically classified into two categories based on defect locations. A defect in a library cell is called a cell internal defect, and a defect on interconnecting wires is called an interconnect defect. Defects can also be classified into open defects and bridge defects. Various fault models are developed to address different fault effects and layout information is incorporated into the diagnosis process. The improved diagnosis accuracy and resolution enable failure analysis engineers to focus on smaller areas. This leads to higher PFA success rates and lower turnaround time and costs.
Despite advancements in scan diagnosis, diagnosis resolution or accuracy is still not sufficiently high. Multiple defect suspects are typically reported for each failing integrated circuit device. Some of these defect suspects may be real defects while the other may be fake defects (false positives). The real defects may include random defects and systematic defects. The latter is critical for the yield improvement. Moreover, each of the defect suspects can involve multiple physical features such as bridging defects on several physical layers. It would further improve PFA success rates and reduce turnaround time and costs if a small handful of physical features (root causes) explaining most if not all of the defects could be identified. Using an unsupervised machine learning technique to analyze diagnosis results for volume failing dies, root cause deconvolution can achieve this goal.
Conventional root cause deconvolution is based on scan diagnosis for only interconnect defect suspects and cell defect suspects. Internal defects within a cell are represented by a cell defect model. This limits the effectiveness of both scan diagnosis and root cause deconvolution. As the semiconductor technology nodes move from 14 nm to 10 nm, 7 nm and beyond, cell internal defects occur more often than before. Direct combination of conventional root cause deconvolution and cell-aware diagnosis, however, often cannot identify a correct root cause distribution.